Parkinson’s disease (PD) causes gait impairments resulting in tremor, balance instabilities, increased fall risk, and disability. The current clinical diagnosis success rate is around 80%. Thus, automated classification of these impairments in gait with machine learning techniques can serve as an assessment tool for identification of PD. The primary focus of the study is to investigate anthropometric parameter-based models for classification of non-PD and PD subjects together with their severity. The proposed work performs the computation of clinically relevant features using Vertical Ground Reaction Force (VGRF) data from a total of 165 individuals’ database consisting 93 Parkinson’s and 72 healthy controls. All extracted features are tested for significance and redundancy among other gait characteristics. The optimal combination of features is selected using Recursive Feature Elimination technique with 10-fold cross validation for classification. In this study, wide range of machine learning techniques from different domains are used and their performance is evaluated based on accuracy, specificity, recall, precision and F1-score. Both gender and age-gender specific models outperformed the generalized model for PD as well as PD severity assessment. The highest prediction accuracy reported for age-gender specific models is 98.50% (non-PD and PD) using Support Vector Machine (SVM) classifier and 97.76% (non-PD and PD with severity scales) using k-Nearest Neighbor (kNN) and SVM. This study demonstrates integration of gait data with machine learning techniques as a potential biomarker for assessment of PD severity.

帕金森氏病（PD）导致步态障碍，导致震颤，平衡不稳，跌倒风险增加和致残。目前的临床诊断成功率约为80％。因此，利用机器学习技术对这些障碍进行步态的自动分类可以作为评估PD的评估工具。该研究的主要重点是研究基于人体测量学参数的模型，以对非PD和PD受试者及其严重程度进行分类。拟议的工作使用垂直地面反作用力（VGRF）数据执行临床相关特征的计算，该数据来自165个个体的数据库，其中包括93个帕金森氏病和72个健康对照。测试所有提取的特征是否具有其他步态特征的重要性和冗余性。使用递归特征消除技术和10倍交叉验证进行分类，以选择特征的最佳组合。在这项研究中，使用了来自不同领域的广泛的机器学习技术，并根据准确性，特异性，召回率，准确性和F1分数评估了它们的性能。性别和年龄性别特定模型均优于PD和PD严重性评估的通用模型。使用支持向量机（SVM）分类器报告的针对性别的特定模型的最高预测准确性为98.50％（非PD和PD），使用k最近邻（kNN）为97.76％（非PD和PD具有严重性等级）和SVM。这项研究表明，步态数据与机器学习技术的集成是评估PD严重程度的潜在生物标记。